US5627040A - Flow cytometric method for autoclustering cells - Google Patents

Flow cytometric method for autoclustering cells Download PDF

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US5627040A
US5627040A US08/039,465 US3946593A US5627040A US 5627040 A US5627040 A US 5627040A US 3946593 A US3946593 A US 3946593A US 5627040 A US5627040 A US 5627040A
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cells
boundary surface
cluster
geometric boundary
vector
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Pierre Bierre
Ronald A. Mickaels
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Becton Dickinson and Co
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/5005—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving human or animal cells
    • G01N33/5094—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving human or animal cells for blood cell populations
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/53—Immunoassay; Biospecific binding assay; Materials therefor
    • G01N33/569—Immunoassay; Biospecific binding assay; Materials therefor for microorganisms, e.g. protozoa, bacteria, viruses
    • G01N33/56966—Animal cells
    • G01N33/56972—White blood cells
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/23—Clustering techniques
    • G06F18/232—Non-hierarchical techniques
    • G06F18/2321—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/23—Clustering techniques
    • G06F18/232—Non-hierarchical techniques
    • G06F18/2321—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions
    • G06F18/23211—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions with adaptive number of clusters
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/60—Type of objects
    • G06V20/69—Microscopic objects, e.g. biological cells or cellular parts
    • G06V20/698—Matching; Classification
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N15/00—Investigating characteristics of particles; Investigating permeability, pore-volume or surface-area of porous materials
    • G01N15/10—Investigating individual particles
    • G01N15/14—Optical investigation techniques, e.g. flow cytometry
    • G01N2015/1488—Methods for deciding
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H10/00—ICT specially adapted for the handling or processing of patient-related medical or healthcare data
    • G16H10/40—ICT specially adapted for the handling or processing of patient-related medical or healthcare data for data related to laboratory analysis, e.g. patient specimen analysis
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y10—TECHNICAL SUBJECTS COVERED BY FORMER USPC
    • Y10S—TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y10S435/00—Chemistry: molecular biology and microbiology
    • Y10S435/808—Optical sensing apparatus
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y10—TECHNICAL SUBJECTS COVERED BY FORMER USPC
    • Y10S—TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y10S436/00—Chemistry: analytical and immunological testing
    • Y10S436/80—Fluorescent dyes, e.g. rhodamine
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y10—TECHNICAL SUBJECTS COVERED BY FORMER USPC
    • Y10S—TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y10S436/00—Chemistry: analytical and immunological testing
    • Y10S436/805—Optical property

Definitions

  • This invention relates to a method for classifying multi-parameter data in real time (or from recorded data) into cluster groups for the purpose of defining different populations of particles in a sample.
  • This invention is particularly useful in the field of flow cytometry wherein multi-parameter data is recorded for each cell that passes through an illumination and sensing region. It is especially useful for classifying and counting immunofluorescently labeled CD3, CD4 and CD8 lymphocytes in blood samples from AIDS patients.
  • Particle analysis generally comprises the analysis of cells, nuclei, chromosomes and other particles for the purpose of identifying the particles as members of different populations and/or sorting the particles into different populations.
  • This type of analysis includes automated analysis by means of image and flow cytometry.
  • the particle such as a cell
  • the particle may be labeled with one or more markers and then examined for the presence or absence of one or more such markers.
  • the marker can be directed to a molecule on the cell surface or to a molecule in the cytoplasm. Examination of a cell's physical characteristics, as well as the presence or absence of marker(s), provides additional information which can be useful in identifying the population to which a cell belongs.
  • Cytometry comprises a well known methodology using multi-parameter data for identifying and distinguishing between different cell types in a sample.
  • the sample may be drawn from a variety of biological fluids, such as blood, lymph or urine, or may be derived from suspensions of cells from hard tissues such as colon, lung, breast, kidney or liver.
  • cells are passed, in suspension, substantially one at a time through one or more sensing regions where in each region each cell is illuminated by an energy source.
  • the energy source generally comprises an illumination means that emits light of a single wavelength such as that provided by a laser (e.g., He/Ne or argon) or a mercury arc lamp with appropriate filters.
  • Light at 488 nm is a generally used wavelength of emission in a flow cytometer having a single sensing region.
  • multiple light collection means such as photomultiplier tubes (or "PMT"), are used to record light that passes through each cell (generally referred to as forward light scatter), light that is reflected orthogonal to the direction of the flow of the cells through the sensing region (generally referred to as orthogonal or side light scatter) and fluorescent light emitted from the cell, if it is labeled with fluorescent marker(s), as the cell passes through the sensing region and is illuminated by the energy source.
  • Each of forward light scatter (or FSC), orthogonal light scatter (SSC), and fluorescence emissions (FL1, FL2, etc.) comprise a separate parameter for each cell (or each "event”).
  • FSC forward light scatter
  • SSC orthogonal light scatter
  • FL1, FL2, etc. comprise a separate parameter for each cell (or each "event”).
  • two, three or four parameters can be collected (and recorded) from a cell labeled with two different fluorescence markers.
  • Flow cytometers further comprise data acquisition, analysis and recording means, such as a computer, wherein multiple data channels record data from each PMT for the light scatter and fluorescence emitted by each cell as it passes through the sensing region.
  • the purpose of the analysis system is to classify and count cells wherein each cell presents itself as a set of digitized parameter values.
  • the data collected in real time is plotted in 2-D space for ease of visualization. Such plots are referred to as "dot plots" and a typical example of a dot plot drawn from light scatter data recorded for leukocytes is shown in FIG. 1 of U.S. Pat. No. 4,987,086.
  • 4,727,020, 4,704,891, 4,599,307 and 4,987,086 describe the arrangement of the various components that comprise a flow cytometer, the general principles of use and one approach to gating on cells in order to discriminate between populations of cells in a blood sample.
  • CD4 + T lymphocytes play an important role in HIV infection and AIDS. For example, counting the number of CD4 + T lymphocytes in a sample of blood from an infected individual will provide an indication of the progress of the disease. A cell count under 400 per mm 3 is an indication that the patient has progressed from being seropositive to AIDS. In addition to counting CD4 + T lymphocytes, CD8 + T lymphocytes also have been counted and a ratio of CD4:CD8 cells has been used in understanding AIDS.
  • a sample of whole blood is obtained from a patient.
  • Monoclonal antibodies against CD3 a pan-T lymphocyte marker
  • CD4 and CD8 are labeled directly or indirectly with a fluorescent dye. These dyes have emission spectra that are distinguishable from each other. (Examples of such dyes are set forth in example 1 of U.S. Pat. No. 4,745,285.)
  • the labeled cells then are run on the flow cytometer and data is recorded. Analysis of the data can proceed in real time or be stored in list mode for later analysis.
  • the autoclustering method described herein as the "gravitational attractor engine" addresses the need to automatically assign classifications to multi-parameter events as they arrive from an array of sensors such as the light collection means of a cytometer. It also functions in the post-classification of recordings of multi-parameter events in list-mode or database format. It is particularly useful in clustering Z-parameter data from CD3 and CD4 as well as CD3 and CD8 T cells labeled with immunofluorescent markers in blood samples from AIDS patients.
  • the gravitational attractor consists of a geometric boundary surface of fixed size, shape and orientation, but of variable position, a computational engine by which the boundary surface positions itself optimally to enclose a cluster of multi-parameter events.
  • Multiple attractors may be employed simultaneously for the purposes of classifying multiple clusters of events within the same datastream or recorded data distribution, the strategy being to assign one attractor per population to be identified and/or sorted.
  • Classification of events in the datastream consists of a two-step process: In the first step (pre-analysis), the datastream is analyzed for purposes of precisely centering each attractor's membership boundary surface about the statistical center-of-mass of the data cluster (i.e., population) it is intending to classify.
  • Pre-analysis is terminated after a pre-determined number of events have been analyzed or if significant deviations in an attractor position is found.
  • each attractor's membership boundary is "locked down in place", and incoming datastream events are tested against membership boundaries for classification inclusion vs. exclusion.
  • Major benefits of the gravitational attractor engine are that it: 1) requires no list-mode recording of events in the process of their classification (i.e., data may be analyzed in real time); 2) provides a classification method tolerant of between-sample drift in the central value of a data cluster which may arise from any arbitrary combination of instrumentation, sample-preparation and intrinsic sample variance sources; 3) exhibits stability in the case of multiple missing clusters and can count particles in a population down to absolute zero in the vicinity of where the cluster is expected to locate; and 4) provides continuous access to population vector means and membership counts during sampling of the datastream, allowing continuous process quality assurance (or "PQA") during time-consuming, rare-event assays.
  • PQA process quality assurance
  • hyperspherical boundary surfaces can be elongated on a preferred axis to obtain a cigar-shaped attractor; 2) the boundary surface used to gate events for gravitational interaction during pre-analysis can be different in shape and extent from the membership boundary applied during classification; and 3) the subset of parameters used to cluster events can be different for different attractors, allowing smear-inducing parameters to be ignored and permitting data classification at varying degrees of dimensional collapse.
  • the primary advantage of the gravitational attractor engine is its capacity for accurate and efficient autoclustering, that is, it can replace manual-clustering methods which require human judgment to adapt gating geometry to normal variances in the positions of target clusters.
  • prior autoclustering methods which rely on histogram curve analysis to locate threshold-type separators are less-robust in the handling of missing populations (especially multiple missing clusters).
  • a cigar-shaped attractor engine performs well at classifying diagonally-elongated clusters whose "stretch" originates from partially-correlated (i.e., uncompensated) events.
  • prior methods utilizing 1-D histogram analysis do not work as well with uncompensated clusters because their 1-D histogram projections consume excessive curvespace.
  • an attractor can be defined in arbitrary N-dimensional space, the problem of overlapping clusters may be redressed through the addition of extra parameters to tease them apart at no additional computational complexity.
  • the attractor engine's memory requirements are small and unrelated to the datastream length being sampled, thus making practicable routine analyses in which several million events are sampled.
  • the salient benefit of such mega-assays in cellular diagnostics is to detect diseased cells at thresholds as low as 1 per million normal cells (i.e., rare-event assays), thus, enabling earlier detection and milder interventions to arrest disease.
  • FIGS. 1A and 1B illustrate two multi-dimensional attractors (one spherical and one cigar-shaped) at their seed locations in multi-space prior to pre-analysis.
  • FIGS. 2A and 2B illustrate the same two attractors, by the same projection scatterplots, at their center-of-mass locations in multi-space during classification.
  • FIGS. 3A, 3B and 3C comprise a series of colored 2-D dot plots of FSC vs. SCC (FIG. 3A), log fluorescence FITC vs. log fluorescence PE (FIG. 3B), and log fluorescence FITC vs. log fluorescence (FIG. 3C) for data collected in list mode from erythrocyte whole blood to which different fluorescently labeled monoclonal antibodies have been added.
  • the three gravitational attractors and their respective seed locations are shown prior to autoclustering.
  • the blue dots and boundaries identify the NK cell attractor; the red dots and boundaries identify B lymphocyte attractor; and the green dots and boundaries identify T lymphocyte attractor.
  • FIGS. 4A, 4B and 4C comprise the colored 2-D dot plots as set forth in FIGS. 3A, 3B and 3C post analysis showing the autoclustered populations and final positions of the attractors.
  • the gray dots represent unclustered events (e.g., monocytes, granulocytes and debris) in the sample.
  • FIGS. 5A and 5B comprise two dot plot of log PE version by PE/Cy5 fluorescence showing three autoclustered populations from a sample of unlysed whole blood from a AIDS patient to which a solution containing a known concentration of fluorescently labeled microbeads and fluorescently labeled (FIG. 5A) anti-CD3 and anti-CD4 monoclonal antibodies or (FIG. 5B) anti-CD3 and anti-CD8 monoclonal antibodies have been added.
  • FIGS. 6A and 6B comprise a dot plot as in FIGS. 5A and 5B, however, the blood is taken from a normal individual but the sample has been rejected by PQA.
  • a gravitational attractor is a small computational "engine”. Initially, it contains one or more geometric parameters set by the user for each type of sample to be analyzed or fixed to define an expected target cluster's shape, size and approximate location.
  • the attractor engine further comprises a method for locating a cluster's actual center-of-mass in the datastream being analyzed, and to subsequently classify events in the arriving datastream which satisfy the attractor's geometric membership predicate.
  • the term "gravitational” is apt because the attractor finds its optimal location enclosing the data cluster by falling to its center-of-mass location under the accumulative gravitational force of events in proximity to its expected location in multi-space.
  • the term "attractor”, drawn from dynamical systems theory, refers to the behavior of a system whereby a multitude of initial state vectors move toward, and converge upon a common, equilibrium end-state vector.
  • the state vector corresponds to the instantaneous vector location of a roving geometric boundary surface (specifically a rigidly-attached reference point within it), as the boundary moves from an initial, expected "seed” location to equilibrium at a data cluster's actual center-of-mass location.
  • the gravitational attractor described below illustrates the simplest case of membership geometry, the hypersphere.
  • the engine of a spherical attractor comprises the following fixed and variable components:
  • the invariant aspects of the target cluster are first specified in terms of seed location, s, and radius, r.
  • the specifications of s and r are made by observing projections of the cluster in 2-D projection scatterplots, whereby two coordinates of s are adjusted at a time using an 2-D locator device, and r is edited by "pulling" on its appearance with a locator device until satisfactory.
  • the events in the datastream encountered consist of a variable number of multi-parameter events e i where i indexes the number (or sequence) of the event in the stream and e is the vector of parameter values comprising that particular event.
  • c Prior to analyzing the datastream, c is initialized to the seed location, s.
  • Attractor autoclustering of the datastream comprises a two-step process:
  • pre-analysis the datastream is analyzed for purposes of precisely centering the attractor's membership boundary surface about the statistical center-of-mass of the data cluster it is intending to classify.
  • the spherical attractor transforms each event vector into its own local coordinate system, whose origin is based at c: ##EQU1##
  • the attractor decides whether local e i is short enough in length (e i is close enough in proximity to c) to be allowed attractive pull on c.
  • the interaction gating predicate, g evaluates affirmatively if local e i has vector length less than r:
  • the center-of-mass of a lone cluster in an otherwise vacuous dataspace can be defined simply as the vector-mean of all N event vectors, e i : ##EQU2##
  • each cluster applies its interaction gating function, g, whose job is to protect its centroid calculation from the influence of density pockets elsewhere in space:
  • the attractor's centroid, c is updated on a fixed schedule at prescribed interaction count milestones (i.e., s1, s2, s3 . . . sm). For this purpose, the attractor keeps a running vector sum sigma of all its interacting event vectors. At the start of pre-analysis, sigma and n are zeroed.
  • each arriving event vector which satisfies the above gating predicate is accumulated, by vector addition, into sigma, the interaction count n is incremented, ##EQU3## and if n is one of the scheduled update milestones (e.g., S1) the centroid is updated
  • c is the running vector sum sigma, scalar divided by n.
  • c contains the vector mean of all events which have so far interacted with the attractor.
  • This new, refined value of c which carries the weight of more data than did the its previous value, governs subsequent interaction gating until the next update milestone is reached.
  • the initial seed point, s serves as a default centroid to get the calculation started. It should reflect the best available information about expected cluster position.
  • the computed centroid, c replaces s as the best-available central value for anchoring the interaction gate.
  • the update schedule for c subserves the goal of only improving its accuracy over time.
  • the first attractor update milestone s1 is called the threshold of inertia. It must be overcome in order to replace the seed value s, as a check on wandering. If a cluster were depleted down to a handful of events, and the centroid were allowed to update on the first gated event, and that event fell just inside the gate, the updated gate could be dislocated up to a distance r from the seed point, possibly excluding centrist events from further consideration. If the threshold of inertia cannot be surmounted, no positional refinement is allowed (i.e., the seed value, s, specifies default emplacement of cluster membership geometry). Consequently, clusters which have become so depleted that no density landmark can be established are default-gated about the point where they were expected to have been found.
  • centroid updates e.g., every 50 interactions
  • the cessation of the pre-analysis activity for a single attractor is triggered by either attainment of the number of interactions, sm, specified as the final scheduled update milestone (the attractor's "interaction quota"), or a global time-out metered in time or total events acquired, which ever comes first. If multiple attractors are interacting with the pre-analysis datastream, attractors which have reached their interaction quotas lay dormant while awaiting the attainment of quota by all other attractors, or the global time-out, whichever comes first.
  • Attractor-based autoclustering is a 2-step process.
  • “classification” each attractor's hyperspherical membership boundary is locked down in place at its centroid, c, frozen after the last pre-analysis centroid update was completed (or at s if no update took place).
  • the incoming event is tested against each membership boundary for classification inclusion vs. exclusion, and a membership count incremented at each inclusion decision. If multiple classification and counting of the same event is unnatural or undesirable, one provides a contention resolving mechanism to assure that each event is classified and counted but by one attractor.
  • prioritized classifications Another is to award membership based on closest Euclidean proximity.
  • One distinct advantage of prioritized classifications is that it can easily extend to attractors with more complex geometry's which can overlap in more complex ways, and for this reason it has been adopted into practice.
  • the membership count so far accumulated by each attractor is available for deciding when enough target events have been counted to terminate the assay.
  • These accretional counts may be used for early detection of missing clusters, for example, indicating a sample-preparation omission which is cause for aborting the assay.
  • the cessation of classification is triggered by attainment of "membership quotas" for all attractors, or a global time-out expressed in time or total acquired events during the classification phase.
  • each attractor holds its cluster population count and centroid (location) vectors, and thus, provides additional benefits to data analyses.
  • Such benefits include quality-assurance mechanisms by which the user can define acceptable vs. aberrant datastream distributions, and automatically have the latter flagged.
  • a "minimum expected population" (defined apriori for each cluster as a function of membership count or a derivative thereof) is compared to the actual membership counts (or a derivative thereof) during, and after termination of, classification. An error condition or warning is generated for each cluster evidencing an unexpectedly low population.
  • This type of PQA benefits from the unique missing cluster stability of the gravitational attractor classification method (i.e., the attractor will accurately count down to absolute zero the occurrence of events in the vicinity where the cluster was expected to have presented itself).
  • a check on attainment of minimum expected population per each target cluster makes the overall autoclustering system vigilant to any number of instrumentation, sample preparation, and intrinsic sample aberration that express as absent target population(s).
  • a "tether” may be employed to define the permissible roving distance of each attractor from its seed position.
  • a tether length (defined apriori and expressed as a scalar distance in multi-space) is compared to the actual displacement of c from its starting seed location, s, to determine if the tether length has been exceeded. If exceeded, an error or warning is generated indicating that a cluster has been found too far from its expected location.
  • a test on proximity of actual cluster (vector mean) location to expected cluster location, per each target cluster makes the overall autoclustering system vigilant to any number of instrumentation, sample preparation, and intrinsic sample aberration that express as unreasonable displacements in multi-space cluster location.
  • the attractor method has the unique advantage of requiring no list-mode recording. Because the tether constraint can be checked each time the attractor moves its position during pre-analysis, it is practical to detect cluster position aberrance early in exposure to the datastream, thus a time-consuming mega-assay can be interrupted early on, rather than waiting until its completion to find out it must be rejected for PQA reasons.
  • a well formal cluster should consist of a dense area of events surrounded by a void region.
  • an orbital band can be placed around the cluster membership boundary. The purpose of the orbital band is to guard against the movement of a cluster too far from its boundary, an unexpected change in the shape of a cluster and higher than expected noise. In any or all of such situations, a high number of events within the orbital band (orbiters") is an indication that the data may be unacceptable. Generally, less than 3% of ten events for a cluster should fall within the orbital band.
  • FIGS. 1A and 1B the centroid (1), radius (2) and orbital band (2) are shown for a spherical attractor.
  • the thickness of the orbital band is arbitrary.
  • a "thin" band will include fewer orbiters than a "thick” band.
  • FIGS. 2A and 2B shows the movement of all of the components during classification.
  • a limitation of the hyperspherical attractor i.e., it's not being well-fitted to the elongated shape of many multi-space data clusters
  • a modification to the gating (or boundary surface) geometry The characteristic of the attractors, whereby each employs an interaction gating function, g whose job is to protect its centroid calculation from the influence of events in other clusters, makes it advantageous to deploy gating geometries that closely approximate actual cluster shape. Better fitting boundaries allow the targeting of more populations within a fixed-size dataspace.
  • An adaptation that elongates the spherical attractor is to replace its centroid vector, c, with a straight line segment in multi-space running between two endpoint vectors, e 1 and e 2 .
  • the line connecting the two endpoints is called the attractor's "centerline”.
  • proximity is measured in terms of distance from the nearest point on the centerline.
  • the locus of points equidistant from the centerline gives rise to a boundary surface that is a hypercylinder with rounded ends. In 3-D space, this solid assumes the shape of a cigar.
  • the cigar attractor's radius, cr specifies both the cigar's cylindrical radius and the radius of curvature of its endcaps.
  • the midpoint, mp, of the centerline is the center of the cigar, and serves as the origin of the cigar's local coordinate system.
  • the cigar attractor's interaction gating function g(e i ) for event e i is:
  • the distance function first finds p, the nearest point on centerline to e i (the projection of e i onto centerline). If p projects beyond the end of the centerline, the distance to the closest endpoint is computed, otherwise the distance between the p and e i is used.
  • the new midpoint mp assumes the value of the gated vector mean of all events which have thus far interacted.
  • the endpoints of the centerline, maintaining rigid values in local coordinates, receive the same delta vector as was applied to mp, thus the centerline moves as a rigid structure under the pull of combined gravitational event force on its midpoint.
  • the cigar membership gating function applied during classification is the same as g(e i ) above.
  • the proximity function and centerline update are the only aspects of the cigar attractor that differ from the spherical attractor. All other behaviors are identical.
  • a more practical approach, reduced to practice in this invention, is to cluster directly on raw, uncompensated event vectors employing a cigar attractor oriented along the principal stretch vector of the cluster in multi-space.
  • the specification of the centerline endpoints is made by observing projections of the cluster in 2-D projection scatterplots, whereby two coordinates of the endpoint are adjusted at a time using an 2-D locator device.
  • the specification of cr is edited by "pulling" on its appearance with a locator device until satisfactory.
  • FIGS. 1A and 1B the center (3), radius (4) and orbital bands are shown for a cigar attractor.
  • FIGS. 2A and 2B show the movement of these components during classification.
  • a slightly different geometry (other than cigar-shaped) suitable for elongated clusters is the hyperellipse.
  • the attachment of an elliptical boundary surface to the attractor behavior claimed herein will be referred to as the elliptical attractor.
  • the orientation axis of the ellipse is specified by its two loci vectors f 1 and f 2 .
  • the proximity of an event is measured in terms of the sum of its two Euclidean distances from the two foci, and the ellipse radius, er, specifies the upper limit of this sum for the event inclusion.
  • the midpoint, mp, of the orientation axis is the center of the ellipse, and serves as its local coordinate system origin.
  • the specification of the principal axis and the gating function are the only two aspects of the elliptical attractor that differentiate it from the cigar attractor. As in the case of the cigar attractor, positional deltas applied to the midpoint propagate to each foci so that the ellipse can maintain its fixed orientation, size and shape.
  • an attractor's classification geometry is to suitably enclose its target cluster's event cloud when deployed at its center-of-mass.
  • the purpose of its interaction geometry is to define a "seek area" in which the attractor can expect to find its cluster (and little else). Since these two geometry's serve differing purposes, it is sometimes advantageous to customize the geometry's subserving interactions and classifications.
  • a spherical attractor may employ a "membership radius” different from its “interaction radius”.
  • Other geometry's can be invoked for defining an attractor's interaction and membership boundaries (i.e., squares, rectangles, tilted rectangles, ellipses or arbitrary mouse-drawn regions).
  • cluster membership boundaries are chosen to approximate the actual size and shape of their target clusters.
  • Attractor interaction boundaries are chosen that both 1) delimit the scan area where center-of-mass should be found and 2) exclude neighboring clusters from possible interaction.
  • An attractor can be defined on a subset of arriving parameters. Different attractors may be defined on different subsets of arriving parameters, if useful for clustering their respective populations.
  • a mask, M, or vector of binary switches, is stored within each attractor to signify which parameters of incoming event vectors are to be attended to and which ignored. Since the attractor engine can be defined in any N-dimensional space, it can be defined on a subset of parameters without embellishment beyond the mere requirement to specify M.
  • the vector operations that underlie the attractor engine are implemented in such a way that masked out parameters are treated as non-existent in a completely transparent fashion.
  • parameter masking permits data clusters to be defined in the subset of parameters which affords the sharpest cluster definition, 2) allows parameters to be ignored which smear an otherwise well-formed cluster and 3) support classification at varying degrees of dimensional collapse.
  • the latter benefit requires that a single event be permitted to be classified by multiple attractors.
  • peripheral whole blood was obtained from normal adult volunteers in EDTA containing evacuated blood collection tubes.
  • Erythrocyte were lysed in a lysing solution comprising NH 4 Cl, KHCO 3 and EDTA. The lysed cells were spun down and removed.
  • the remaining cells were placed in a test tube containing PBS. To this tube were added, in sequence, Leu 4 FITC (anti-CD3; BDIS), Leu 11+19 PE (anti-CD16, CD56; BDIS) and Leu 12 PerCp (anti-CD19; BDIS). These antibodies will label T lymphocytes, NK cells and B lymphocytes respectively. After incubation the cells were washed and then run on a FACScan brand flow cytometer (BDIS) equipped with Consort FACScan Research Software (BDIS). The data was acquired and stored in list-mode. 15,000 events were recorded.
  • BDIS FACScan brand flow cytometer
  • BDIS Consort FACScan Research Software
  • each population's attractor was identified prior to analysis based upon well known and published data.
  • a spherical attractor was applied for B lymphocytes while cigar attractors were used for NK cells and T lymphocytes.
  • Each attractor then was mouse drawn to represent the expected locations of each population when the data was analyzed for scatter (FIG. 3A), PE vs. FITC fluorescence (FIG. 3B) and PerCp vs. FITC fluorescence (FIG. 3C). Gray dots are shown interposed on the dot plots showing unclustered events. (In other embodiments, it will be appreciated that these unclustered events need not be displayed in either real time or list-mode analysis.)
  • FIGS. 4A, 4B and 4C the results of classification are displayed after all recorded events have been analyzed.
  • FSC FSC
  • SSC SSC
  • log PE fluorescence log FITC fluorescence
  • log PerCP fluorescence log PerCP fluorescence.
  • B lymphocytes 757 cells (or approximately 19% of all clustered events) were within this cluster.
  • For T lymphocytes 2596 (or approximately 66% of all clustered events) were within the cluster; and for NK cells, 587 events were within this cluster. It should be appreciated that the data analysis for all of the attractors occurs at the same time.
  • FIGS. 4A, 4B and 4C represent the 2-D projections of each attractor post analysis.
  • whole blood was obtained from an AIDS patient (FIGS. 5A and 5B) and from a normal adult volunteer in EDTA containing evaluated blood collection tubes. Each sample was split into two aliquots. A mixture of 50,000 fluorescent microbeads, titered amounts of antibody and buffer to make 400 ⁇ l was prepared for each aliquot. To one aliquot from each sample the antibodies consisted of Leu 4 PE/Cy5 and Leu 3a PE. (Cy5 was obtained from Biological Detection Systems.) To the other aliquot from each sample the antibodies consisted of Leu 4 PE/Cy5 and Leu 2a PE.
  • CD8 cells Three elliptical attractors were applied to the bead, CD4 - and CD4 + or CD8 - and CD8 + clusters.
  • One difficulty encountered in the analysis of CD8 cells is that, unlike CD4 cells, CD8 cells do not differentiate into well defined positive and negative clusters. A small number of CD8 cells will appear to be "dim.” These dim cells are CD8 + and therefore must included in the count if the absolute is to be accurate.
  • a new clustering tool was developed to solve this problem.
  • a "pipe” is drawn connecting the upper (i.e., CD8 + ) cluster with the lower (i.e., CD8 - ) cluster. It is drawn so that in a 2-D plot one side extends from the left most edge of the upper cluster boundary to the left most edge of the lower cluster boundary and the other side extends from right most edge of the upper cluster boundary to the right most edge of the lower cluster boundary. Any events falling within the orbital bands surrounding the cluster boundaries of the pipe are monitored as a PQA check assuring proper containment of CD8 dim cells and as a PQA check against encroachment by debris.
  • This may be accomplished by a two-step analysis where only beads are analyzed initially in order to establish the bead peak or by means of analysis of a control tube prior to actual sample acquisition.
  • a circular attractor is employed to establish the bead peak while an elliptical attractor is employed in the analysis step.
  • FIGS. 5A and 5B display the final positions of the clusters and the events that fell within each cluster for whole blood from an AIDS patient.
  • the majority of events within a cluster occur within the CD4 - or CD8 - clusters. There are few events that fall outside the cluster that are not either CD4 + or CD4 - T cells or beads.
  • the events are distributed in a manner similar to CD4 + cells; however, the pipe region is applied to collect those CD8 + cells that express "dim" amounts of fluorescence.
  • Table I sets forth the numbers of events that fell within each cluster as well as those non-red blood cell events that were not clustered.
  • the number of CD4 + cells per ⁇ l of whole blood was calculated as 156; the number of CD3 + cells per ⁇ l of whole blood was calculated to as 972 in the CD4 tube and 978 in the CD8 tube; and the number of CD8 + cells per ⁇ l of whole blood was calculated as 769.
  • the number of cells in the orbital bands was low confirming the integrity of the cluster.
  • FIGS. 5A and 5B are to be compared with the data from FIGS. 6A and 6B to show how this invention provides PQA.
  • FIG. 6A it can be seen that the CD4 - cluster is contaminated with debris and red blood cells, whereas in FIG. 5A there is a separation between the red blood cells/debris and the CD4 - cells.
  • This problem also shows up in Table II where the number of events occurring in the orbital bands for CD4 - and CD8 - is higher than should be expected if cluster integrity had been maintained. Based on this data, the sample in FIGS. 6A and 6B should have been rejected.

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ATE151546T1 (de) 1997-04-15
EP0554447A4 (fr) 1994-03-30
ES2102518T3 (es) 1997-08-01
US5795727A (en) 1998-08-18
DE69218912D1 (de) 1997-05-15
EP0554447A1 (fr) 1993-08-11
DE69218912T2 (de) 1997-10-09
JPH06501106A (ja) 1994-01-27
JP2581514B2 (ja) 1997-02-12
EP0554447B1 (fr) 1997-04-09
WO1993005478A1 (fr) 1993-03-18

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